A suite of commands for fitting the skew-normal and skew-t models
Abstract. Nonnormal data arise often in practice, prompting the development
of flexible distributions for modeling such situations. In this article, we describe
two multivariate distributions, the skew-normal and the skew-t, which can be
used to model skewed and heavy-tailed continuous data. We then discuss some
inferential issues that can arise when fitting these distributions to real data. We
also consider the use of these distributions in a regression setting for more flexible
parametric modeling of the conditional distribution given other predictors. We
present commands for fitting univariate and multivariate skew-normal and skew-t
regressions in Stata (skewnreg, skewtreg, mskewnreg, and mskewtreg) as well as
some postestimation features (predict and skewrplot). We also demonstrate the
use of the commands for the analysis of the famous Australian Institute of Sport
data and U.S. precipitation data.
View all articles by these authors:
Yulia V. Marchenko, Marc G. Genton
View all articles with these keywords:
skewnreg, skewtreg, mskewnreg, mskewtreg, skewrplot, predict, distribution, heavy tails, nonnormal, precipitation, regression, skewness, skew-normal, skew-t
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